{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c5148957",
   "metadata": {},
   "source": [
    "# Exogenous variables (features)\n",
    "\n",
    "\n",
    "Exogenous variables are predictors that are independent of the model being used for forecasting, and their future values must be known in order to include them in the prediction process. The inclusion of exogenous variables can enhance the accuracy of forecasts.\n",
    "\n",
    "In skforecast, exogenous variables can be easily included as predictors in all forecasting models. To ensure that their effects are accurately accounted for, it is crucial to include these variables during both the training and prediction phases. This will help to optimize the accuracy of forecasts and provide more reliable predictions."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c537f30b",
   "metadata": {},
   "source": [
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/matrix_transformation_with_exog_variable.png\" style=\"width: 500px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Time series transformation including an exogenous variable.</i></font>\n",
    "</p>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "35a5666b",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "When exogenous variables are included in a forecasting model, it is assumed that all exogenous inputs are known in the future. Do not include exogenous variables as predictors if their future value will not be known when making predictions.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a25487",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "For a detailed guide on how to include categorical exogenous variables, please visit <a href=\"../user_guides/categorical-features.html\">Categorical Features</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7eac7f8a",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dfbffdd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from feature_engine.timeseries.forecasting import WindowFeatures\n",
    "from feature_engine.timeseries.forecasting import LagFeatures\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.plot import set_dark_theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97e2fb8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────── <span style=\"font-weight: bold\">h2o_exog</span> ────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health   │\n",
       "│ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │\n",
       "│ simulated.                                                                      │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd      │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,                              │\n",
       "│ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.             │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/h2o_exog.csv                                                 │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 195 rows x 3 columns                                                     │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────────── \u001b[1mh2o_exog\u001b[0m ────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health   │\n",
       "│ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │\n",
       "│ simulated.                                                                      │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd      │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,                              │\n",
       "│ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.             │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/h2o_exog.csv                                                 │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mShape:\u001b[0m 195 rows x 3 columns                                                     │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name='h2o_exog', raw=False)\n",
    "data.index.name = 'datetime'\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(7, 3.5))\n",
    "data.plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "902c3845",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data in train and test\n",
    "# ==============================================================================\n",
    "steps = 36\n",
    "data_train = data.iloc[:-steps, :]\n",
    "data_test  = data.iloc[-steps:, :]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "57c8f8ae",
   "metadata": {},
   "source": [
    "## Train forecaster with exogenous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aee6435e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </style>\n",
       "    \n",
       "        <div class=\"container-f145fda34eef4e06a5e105c7d323672f\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursive</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]</li>\n",
       "                    <li><strong>Window features:</strong> None</li>\n",
       "                    <li><strong>Window size:</strong> 15</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> True</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 15:01:23</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 15:01:24</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    exog_1, exog_2\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1992-04-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <MonthBegin></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursive.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/autoregressive-forecaster.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=================== \n",
       "ForecasterRecursive \n",
       "=================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15] \n",
       "Window features: None \n",
       "Window size: 15 \n",
       "Series name: y \n",
       "Exogenous included: True \n",
       "Exogenous names: exog_1, exog_2 \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Differentiation order: None \n",
       "Training range: [Timestamp('1992-04-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <MonthBegin> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0,\n",
       "    'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 15:01:23 \n",
       "Last fit date: 2025-11-26 15:01:24 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags      = 15\n",
    "             )\n",
    "forecaster.fit(\n",
    "    y    = data_train['y'],\n",
    "    exog = data_train[['exog_1', 'exog_2']]\n",
    ")\n",
    "forecaster"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c720b5f9",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "\n",
    "If the `Forecaster` has been trained using exogenous variables, they should be provided during the prediction phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a07a660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    1.023969\n",
       "2005-08-01    1.044023\n",
       "2005-09-01    1.110078\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(\n",
    "                  steps = 36,\n",
    "                  exog  = data_test[['exog_1', 'exog_2']]\n",
    "              )\n",
    "\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92f4d26f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3.5))\n",
    "data_train['y'].plot(ax=ax, label='train')\n",
    "data_test['y'].plot(ax=ax, label='test')\n",
    "predictions.plot(ax=ax, label='predictions')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab9d1e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error (MSE): 0.005576949968874203\n"
     ]
    }
   ],
   "source": [
    "# Prediction error\n",
    "# ==============================================================================\n",
    "error_mse = mean_squared_error(\n",
    "                y_true = data_test['y'],\n",
    "                y_pred = predictions\n",
    "            )\n",
    "\n",
    "print(f\"Test error (MSE): {error_mse}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4acd3d2f",
   "metadata": {},
   "source": [
    "## Feature importances\n",
    "\n",
    "If exogenous variables are included as predictors, they have a value of feature importances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "243f4bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>lag_12</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>exog_1</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>exog_2</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lag_11</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lag_6</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lag_14</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lag_5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lag_3</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>lag_15</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>lag_13</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lag_4</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lag_2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lag_10</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lag_1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lag_8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lag_7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lag_9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature  importance\n",
       "11  lag_12          66\n",
       "15  exog_1          49\n",
       "16  exog_2          37\n",
       "10  lag_11          36\n",
       "5    lag_6          31\n",
       "13  lag_14          26\n",
       "4    lag_5          26\n",
       "2    lag_3          25\n",
       "14  lag_15          24\n",
       "12  lag_13          23\n",
       "3    lag_4          23\n",
       "1    lag_2          22\n",
       "9   lag_10          18\n",
       "0    lag_1          16\n",
       "7    lag_8          16\n",
       "6    lag_7          15\n",
       "8    lag_9          12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importances with exogenous variables\n",
    "# ==============================================================================\n",
    "forecaster.get_feature_importances()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f6df9a",
   "metadata": {},
   "source": [
    "## Handling missing exogenous data in initial training periods\n",
    "\n",
    "When working with time series models that incorporate exogenous variables, it’s common to encounter cases where exogenous data isn't available for the very first part of the historical dataset. This can raise concerns, especially since these initial observations are essential for creating predictors and training matrices. However, full alignment between the exogenous variables and the time series data is only necessary after this initial window period.\n",
    "\n",
    "In practical terms, this means that if you have missing exogenous values in the early part of your data, they won't prevent model training as long as your exogenous variables are aligned from the point where predictors are created (after the first `window_size` observations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ecbe88c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Window size required by the Forecaster: 15\n"
     ]
    }
   ],
   "source": [
    "# Window required by the Forecaster to create predictors\n",
    "# ==============================================================================\n",
    "window_size = forecaster.window_size\n",
    "print(\"Window size required by the Forecaster:\", window_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6992befe",
   "metadata": {},
   "source": [
    "A exogenous variable which skips the first `window_size` observations of the time series is simulated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0181d09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulate data\n",
    "# ==============================================================================\n",
    "exog_no_first_window_size = data_train[['exog_1', 'exog_2']].copy()\n",
    "exog_no_first_window_size = exog_no_first_window_size.iloc[window_size:, :]\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3.5))\n",
    "data_train[['y']].plot(ax=ax)\n",
    "exog_no_first_window_size.plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b01695f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    1.023969\n",
       "2005-08-01    1.044023\n",
       "2005-09-01    1.110078\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags      = 15\n",
    "             )\n",
    "\n",
    "forecaster.fit(\n",
    "    y    = data_train['y'],\n",
    "    exog = exog_no_first_window_size[['exog_1', 'exog_2']]\n",
    ")\n",
    "\n",
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(\n",
    "                  steps = 36,\n",
    "                  exog  = data_test[['exog_1', 'exog_2']]\n",
    "              )\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "74fa3dc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error (MSE): 0.005576949968874203\n"
     ]
    }
   ],
   "source": [
    "# Prediction error\n",
    "# ==============================================================================\n",
    "error_mse = mean_squared_error(\n",
    "                y_true = data_test['y'],\n",
    "                y_pred = predictions\n",
    "            )\n",
    "\n",
    "print(f\"Test error (MSE): {error_mse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59dae87c",
   "metadata": {},
   "source": [
    "Since the training matrices are the same as those used with the full exogenous variables, the resulting model is the same and the predictions are identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ebd1c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check training matrices are the same with both methods\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags      = 15\n",
    "             )\n",
    "\n",
    "X_train_full_exog, y_train_full_exog = forecaster.create_train_X_y(\n",
    "    y    = data_train['y'],\n",
    "    exog = data_train[['exog_1', 'exog_2']]\n",
    ")\n",
    "\n",
    "X_train_no_full_exog, y_train_no_full_exog = forecaster.create_train_X_y(\n",
    "    y    = data_train['y'],\n",
    "    exog = exog_no_first_window_size[['exog_1', 'exog_2']]\n",
    ")\n",
    "\n",
    "pd.testing.assert_frame_equal(X_train_full_exog, X_train_no_full_exog)\n",
    "pd.testing.assert_series_equal(y_train_full_exog, y_train_no_full_exog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885189d9",
   "metadata": {},
   "source": [
    "## Lagged values and window features from exogenous variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908b160a",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "  This section focuses on lagged values and window features derived from past values of the exogenous variables. These features are different from the window features derived from series being forecasted. See the \n",
    "  <a href=\"../user_guides/window-features-and-custom-features.html\">Window and custom features</a> for more information on the latter.\n",
    "</p>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b93119f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────── <span style=\"font-weight: bold\">bike_sharing</span> ──────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, information   │\n",
       "│ about weather conditions and holidays is available.                             │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_dataset_clean.csv                               │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 17544 rows x 11 columns                                                  │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭───────────────────────────────── \u001b[1mbike_sharing\u001b[0m ──────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Hourly usage of the bike share system in the city of Washington D.C. during the │\n",
       "│ years 2011 and 2012. In addition to the number of users per hour, information   │\n",
       "│ about weather conditions and holidays is available.                             │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository.   │\n",
       "│ https://doi.org/10.24432/C5W894.                                                │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/bike_sharing_dataset_clean.csv                               │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mShape:\u001b[0m 17544 rows x 11 columns                                                  │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>users</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     users  holiday  temp  windspeed\n",
       "date_time                                           \n",
       "2011-01-01 00:00:00   16.0      0.0  9.84        0.0\n",
       "2011-01-01 01:00:00   40.0      0.0  9.02        0.0\n",
       "2011-01-01 02:00:00   32.0      0.0  9.02        0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Downloading data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name='bike_sharing', raw=False)\n",
    "data = data.loc[:, ['users', 'holiday', 'temp', 'windspeed']]\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83a8cf2",
   "metadata": {},
   "source": [
    "Combining the [<code>LagFeatures</code>](https://feature-engine.trainindata.com/en/latest/user_guide/timeseries/forecasting/LagFeatures.html#lagfeatures) and [<code>WindowFeatures</code>](https://feature-engine.trainindata.com/en/latest/api_doc/timeseries/forecasting/WindowFeatures.html) from feature-engine library, it is straightforward to create lagged values and window features from exogenous variables. In this example, the last 3 lagged values aswell as the mean and standard deviation of the last 24 values are extracted from the exogenous variable `temp` and `windspeed`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f22826e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;windowfeatures&#x27;,\n",
       "                 WindowFeatures(freq=&#x27;h&#x27;, functions=[&#x27;mean&#x27;],\n",
       "                                missing_values=&#x27;ignore&#x27;,\n",
       "                                variables=[&#x27;temp&#x27;, &#x27;windspeed&#x27;],\n",
       "                                window=[&#x27;24h&#x27;])),\n",
       "                (&#x27;lagfeatures&#x27;,\n",
       "                 LagFeatures(periods=[1, 2, 3],\n",
       "                             variables=[&#x27;temp&#x27;, &#x27;windspeed&#x27;]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;windowfeatures&#x27;,\n",
       "                 WindowFeatures(freq=&#x27;h&#x27;, functions=[&#x27;mean&#x27;],\n",
       "                                missing_values=&#x27;ignore&#x27;,\n",
       "                                variables=[&#x27;temp&#x27;, &#x27;windspeed&#x27;],\n",
       "                                window=[&#x27;24h&#x27;])),\n",
       "                (&#x27;lagfeatures&#x27;,\n",
       "                 LagFeatures(periods=[1, 2, 3],\n",
       "                             variables=[&#x27;temp&#x27;, &#x27;windspeed&#x27;]))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>WindowFeatures</div></div></label><div class=\"sk-toggleable__content \"><pre>WindowFeatures(freq=&#x27;h&#x27;, functions=[&#x27;mean&#x27;], missing_values=&#x27;ignore&#x27;,\n",
       "               variables=[&#x27;temp&#x27;, &#x27;windspeed&#x27;], window=[&#x27;24h&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>LagFeatures</div></div></label><div class=\"sk-toggleable__content \"><pre>LagFeatures(periods=[1, 2, 3], variables=[&#x27;temp&#x27;, &#x27;windspeed&#x27;])</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('windowfeatures',\n",
       "                 WindowFeatures(freq='h', functions=['mean'],\n",
       "                                missing_values='ignore',\n",
       "                                variables=['temp', 'windspeed'],\n",
       "                                window=['24h'])),\n",
       "                ('lagfeatures',\n",
       "                 LagFeatures(periods=[1, 2, 3],\n",
       "                             variables=['temp', 'windspeed']))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create lagged features and rolling windows features from exogenous variables\n",
    "# ==============================================================================\n",
    "lag_transformer = LagFeatures(\n",
    "                    variables = [\"temp\", \"windspeed\"],\n",
    "                    periods   = [1, 2, 3],\n",
    "                  )\n",
    "\n",
    "wf_transformer = WindowFeatures(\n",
    "                    variables      = [\"temp\", \"windspeed\"],\n",
    "                    window         = [\"24h\"],\n",
    "                    functions      = [\"mean\"],\n",
    "                    freq           = \"h\",\n",
    "                    missing_values = \"ignore\",\n",
    "                    drop_na        = False,\n",
    "                )\n",
    "\n",
    "exog_transformer = make_pipeline(\n",
    "                        wf_transformer,\n",
    "                        lag_transformer\n",
    "                   )\n",
    "\n",
    "exog_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ee97a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>users</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>temp_window_24h_mean</th>\n",
       "      <th>windspeed_window_24h_mean</th>\n",
       "      <th>temp_lag_1</th>\n",
       "      <th>windspeed_lag_1</th>\n",
       "      <th>temp_lag_2</th>\n",
       "      <th>windspeed_lag_2</th>\n",
       "      <th>temp_lag_3</th>\n",
       "      <th>windspeed_lag_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.840000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.430000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.293333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.430000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     users  holiday  temp  windspeed  temp_window_24h_mean  \\\n",
       "date_time                                                                    \n",
       "2011-01-01 00:00:00   16.0      0.0  9.84        0.0                   NaN   \n",
       "2011-01-01 01:00:00   40.0      0.0  9.02        0.0              9.840000   \n",
       "2011-01-01 02:00:00   32.0      0.0  9.02        0.0              9.430000   \n",
       "2011-01-01 03:00:00   13.0      0.0  9.84        0.0              9.293333   \n",
       "2011-01-01 04:00:00    1.0      0.0  9.84        0.0              9.430000   \n",
       "\n",
       "                     windspeed_window_24h_mean  temp_lag_1  windspeed_lag_1  \\\n",
       "date_time                                                                     \n",
       "2011-01-01 00:00:00                        NaN         NaN              NaN   \n",
       "2011-01-01 01:00:00                        0.0        9.84              0.0   \n",
       "2011-01-01 02:00:00                        0.0        9.02              0.0   \n",
       "2011-01-01 03:00:00                        0.0        9.02              0.0   \n",
       "2011-01-01 04:00:00                        0.0        9.84              0.0   \n",
       "\n",
       "                     temp_lag_2  windspeed_lag_2  temp_lag_3  windspeed_lag_3  \n",
       "date_time                                                                      \n",
       "2011-01-01 00:00:00         NaN              NaN         NaN              NaN  \n",
       "2011-01-01 01:00:00         NaN              NaN         NaN              NaN  \n",
       "2011-01-01 02:00:00        9.84              0.0         NaN              NaN  \n",
       "2011-01-01 03:00:00        9.02              0.0        9.84              0.0  \n",
       "2011-01-01 04:00:00        9.02              0.0        9.02              0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = exog_transformer.fit_transform(data)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f900bfb2",
   "metadata": {},
   "source": [
    "## Backtesting with exogenous variables\n",
    "\n",
    "All the backtesting strategies available in skforecast can also be applied when incorporating exogenous variables in the forecasting model. Visit the [Backtesting section](../user_guides/backtesting.html#backtesting-with-exogenous-variables) for more information."
   ]
  }
 ],
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